جستجوی مقالات مرتبط با کلیدواژه "time" در نشریات گروه "فیزیک"

In this paper, aluminum 5052 (AA 5052), one of the candidate alloys for nuclear fuel cladding, has been selected for study regarding its corrosion behavior in an electrolyte containing NaCl and Na2SO4. Potentiodynamic polarization and electrochemical impedance methods were used to investigate the corrosion of AA 5052. The morphology and characterization of corrosion products were carried out using Scanning Electron Microscopy (SEM), X-ray Diffraction (XRD), and Fourier-Transform Infrared Spectroscopy (FTIR) methods. In the Na2SO4 electrolyte, the corrosion resistance of the sample improved over time, with the corrosion current decreasing from 10-5.4 mA/cm2 after 1 hour to 10-6 mA/cm2 after 11 days. AA 5052 showed a wider passivation potential range (ΔEpass) and less passivation current (ipass) after 11 days. In the NaCl electrolyte, pitting corrosion, elimination of the passivation zone, and an increase in the number and size of pits were observed with increased time. These results confirm that the time has an influence on the passivation behavior of AA 5052, primarily dependent on the chemical composition of the electrolyte (presence or absence of chlorine and sulfate anions). Additionally, the application of tensile stress showed a destructive effect on the passive layer, as the passivation behavior of AA 5052 was eliminated.

Many steps of seismic data processing sequence suppose that data sets are sampled in time and spatial dimensions uniformly. Today, this assumption is true but only in time dimension. Modern seismic exploration equipment permits seismic data sets to be sampled uniformly and densely in time dimension. However, along spatial dimension uniform and dense sampling are not possible because of operating constraints, failure of equipment, topography conditions or commercial problems. It has been proved that the results of most of seismic data processing techniques are dependent on regularity, adequate sampling and density of input data sets. The fact that we need to interpolate seismic data sets causes several new-born approaches in this field. In most of the available seismic processing software, this task is done by ‘binning’ the data. This operation is one of the error sources of seismic sections. Moreover, there are some other different computational techniques to interpolate and reconstruct seismic data on a regular grid. Some of these approaches reconstruct seismic data at the given points using physical concepts of wave propagation and solving Kirchhoff's formula. In spite of practicability of these methods, need of initial accurate information about velocity model, geological property and high computational efforts restrict the domain of operation for these methods. Nowadays various mathematical methods are provided using the design of prediction filters, mathematical transformation and some other methods use rank reduction of data matrix to interpolate seismic data. According to their utilized assumptions, computational cost, noise, sampling type, and density of input data, each of these methods have their own constraints in performance and artifacts in final results which should be recognized. In science and engineering branches, a well-known algorithm that deals with signals is Matching Pursuit (MP). Originally, MP has been introduced to time-frequency transformation and finding the frequency content of signals. This transformation represents a signal as a linear composition of vectors that are available in a complete bank of time-frequency atoms (also called Dictionary). MP is an iterative algorithm that at each iteration finds a base vector in the dictionary that best matches to the signal, then subtracts the image of signal along this vector from the signal and updates the signal. This process will be continued until the remained signal is negligible. Originally, to have a good decomposition, this dictionary should contain a vast amount and kinds of wavelets like Gabor functions that each has its own dilation, modulation and translation.Heretofore MP is used to produce a single frequency seismic attribute in geophysics. For seismic data reconstruction and interpolation purposes, sine functions are applied as base vectors. The process of interpolation by MP that uses sine functions needs to solve a Lomb-Scargle periodogram at each iteration that may need to have many computations. Due the lots of works that have been done on this subject, today multi-dimension and multi-component seismic data set can be interpolated using sine functions at MP. Other functions that can be used as MP’s base vectors are Fourier coefficients. Here, after some brief explanation about MP’s algorithm and formulations we use Fourier coefficients as the base vectors of MP, interpolate and reconstruct some synthetic and real two and three dimensional seismic data. Despite of some random noises that are due to calculation and other estimations,the traces are reproduced acceptably. The results show that amplitude and frequency contents of events are well preserved. The noticeable point is that the traces that reproduced at original sampling points are nearly identical to original traces. This property and ability to interpolate data with completely non-uniform sampling grid separates Fourier MP from many of previous interpolation methods. Cautiously picking of several base function simultaneously is proposed to reduce needed iterations and speed up the algorithm. Windowing the input data and using an antialiasing mask are proposed to achieve the assumption of sparse frequency content and linearity of events and remove aliasing effects.

In the dye sensitized solar cell, the dye molecules play the role of light absorption that is a key role in performance of the solar cells. One of the features that affect the optical spectrum of dyes is size of the nano molecules. In this paper, the optical spectrum of nano molecules of coumarin families was calculated with different sizes by using the Time-Dependent Density-Functional Tight-Binding and Time-Dependent Density-Functional methods. In this calculation, the HOMO-LUMO gap increases as the size of the dyes decreases in accordance with the experiment. Our computations confirm the 2 order of speed of first method compared to the second method. Finally, Time-Dependent Density-Functional Tight-Binding method proposed the S-cis Nkx-2311 with ZnO for the simulation of the dye sensitized solar cell.

Seismic attribute is a quantitative measure of an interested seismic characteristic. There are several seismic attributes. In recent years, time-frequency (TF) attributes have been developed which to reach them, TF analyzing of seismic data is required. A high resolution TF representation (TFR) can yield more accurate TF attributes. There are several TFR methods including short-time Fourier transform, wavelet transforms, S-transform, Wigner-Ville distribution, Hilbert-Huang transform and etc. In this paper, the S-transform is considered and an algorithm is proposed to improve its resolution. In the Fourier-based TFR methods, the width of the utilized window is the main factor affecting the resolution. The standard S-transform (SST) employs a Gaussian window which its standard deviation, controller the window width, changes inversely with frequency (Stockwell et al., 1996). It was an idea to use a frequency dependent window for TF decomposition. However, the TF resolution of SST is far from ideal; it demonstrates weak temporal resolution at low frequencies and weak spectral resolution at high frequency components. Later on, the generalized S-transform was proposed using an arbitrary window function whose shape is controlled by several free parameters (McFadden et a., 1999; Pinnegar and Mansinha, 2003). Another approach to improve the resolution of a TFR is based on energy concentration concept (Gholami, 2013; Djurovic et al., 2008). According this approach, in this paper, an algorithm is proposed to find the optimum windows for S-transform to get a TFR with maximum energy concentration. To reach this aim, an optimization problem is defined where an energy concentration measure (ECM) is employed to condition the windows so as the TFR would have the maximum energy concentration. Here, we utilize a Gaussian as the window function. Then different windows are constructed by a range of different values of standard deviations in a non-parametric form. Different TFRs are constructed by different windows. The optimum TFR is one with maximum energy concentration. The optimization is performed for each frequency component, individually, and hence, there would be an optimum window width for each frequency component. There are several ECMs which they are used in different applications (Hurley and Rickard, 2009). In this paper, we employ Modified Shannon Entropy as the ECM. As one knows, SST algorithm needs to be implemented in frequency domain (Stockwell et al., 1996). It is due to the dependency of the standard deviation of Gaussian window on the frequency. However, the proposed method of our paper can also be implemented in time domain where the optimum windows would be found, adaptively, for each time sample of the signal. We apply the proposed method to a synthetic signal to compare its performance with some other TF analysis methods in providing a well-concentrated TF map. The comparison of the results shows the superiority of the proposed method rather than STFT and SST. We also perform a quantitative experiment to evaluate the performance of the TFRs. The results confirm the best performance by the proposed method compared with STFT and SST. Then the proposed method is employed to detect gas bearing zones and low-frequency shadows on a seismic data set related to a gas reservoir of Iran. For this aim, some TF seismic attributes are extracted. The attributes include instantaneous amplitude, dominant instantaneous frequency, sweetness factor, single-frequency section and cumulative relative amplitude percentile (C80). The attributes are also extracted by SST to compare with those of the proposed method. The results show that the attributes obtained by the proposed method have more resolution; so that gas bearing zones and low-frequency shadows are better localized on the attribute sections obtained by the proposed method.

In this study the effect of input parameters of the EDM process including pulse on- time، pulse current and voltage on the output characteristics (material removal rate، tool wear ratio and surface roughness) in the machining AISI D6 tool steel has been studied. This study shows that increasing the pulse on time، material removal rate and the surface roughness are increased and the tool wear ratio is reduced. Also، based on obtained results by increasing pulse current، material removal rate، tool wear ratio and surface roughness are increased.

Scintillation detectors are widely used in the experimental setup for the detection of charged particles. These detectors are able to measure the energy and time-of-flight of the charged particles. Also، they can be used to identify the detected particle. The probability of hadronic interaction between the detected particle and the nuclei of the scintillator atoms is one important issue that must be considered in the analysis of the detectors output. The hadronic interaction causes particles the deposit only a part of their energy inside the detector. In this case، particle will appear in the tail of energy spectrum and is mixed with the background events. Therefore، the measured cross section، which is calculated using the number of particles that deposit their full energy in the detector will be underestimated and one should correct the cross section for the lost events. The percentage of incident particles for which the hadronic interaction occurs is determined by different methods. In this paper، using two different methods، Monte-Carlo simulation and experimental data for several different channels in the proton-deuteron and deuteron-deuteron scattering at intermediate energy are introduced. The obtained results from the two methods are consistent with each other.

Reservoir models are initially generated from estimates of specific rock properties and maps of reservoir heterogeneity. Many types of information are used in reservoir model construction. One of the most important sources of information comes from wells, including well logs and core samples. Unfortunately well log and core data are local measurements that may not reflect the reservoir behavior as a whole. In addition, well data are not available at the initial phases of exploration. In contrast to sparse well data, 3D seismic data cover large areas. Seismic attributes extracted from 3D seismicdata can provide information for the construction of reservoir models. Seismic facies analysis can be accomplished through the use of pattern recognition techniques. When the geological information is incomplete or nonexistent, seismic facies analysis can be done using unsupervised learning techniques. One of the most promising mathematical techniques of unsupervised learning is the Kohonen's Self Organizing Map (SOM) (Kohonen, 2001). In this paper we use the SOM and time-frequency analysis to characterize reservoirs. Since variations in frequency content are sensitive to subtle changes in reflective information. In this context, we show that the wavelet transform modulus maxima line amplitudes (WTMMLA) that extracted from continuous wavelet transforms (CWT) can be applied to detect singularities. These singularities are analyzed and clustered by SOM. The SOM networks map points of input space to points in an output space while preserving the topology. Topology preservation means that points which are close in the input space should also be close in the output space (map). Normally, the input space is of high dimension while the output is two-dimensional. The seismic attributes, can be represented by vectors in the space Rn, x = [x1,x2...xn].We assume that the map has Pelements; therefore, there will exist P prototype vectors mi, mi= [mi1.. . min], i = 1, 2,. .. ,P, where n is the dimension of the input vector. After the SOM training, prototype vectors represent the input data set of seismic attributes, the distances between x and all the prototype vectors are computed. The mapunit with the smallest distance mbto the input vector x is called the best matching unit (BMU) and is computed by,. The prototype vector corresponding to the BMU and their neighbors are moved towards the input winner vector in the input space. Since one of the main objectives of this work was the identification of data clusters, we displayed the distances between the neighbor prototype vectors to identify similarities among the vector prototypes. We used the U-matrix (Ultsch, 1993), to represent these distances. After the SOM learning, the U-matrix was generated by computing, for each SOM prototype vector, the distance between the neighbor prototype vectors and their average. For estimation of the number of existing seismic facies in the data, we used a K-means partitive clustering algorithm. We clustered the prototype vectors instead of the original data. In this manner, large data sets formed by the SOM prototype vectors can be indirectly grouped. Results showed that the proposed method not only provides a better understanding about the group formations, but it is also computationally efficient. Another benefit of this methodology is noise reduction because the prototype vectors represent local averages of the original data without any loss of resolution. To automate the classification process, we used the Davies and Bouldin (1979) index (DBI) as means of evaluating the results of the K-means partitioning. Transitions, or irregular structures, present in any kind of signals carry information related to its physical phenomena (Mallat, 1999). Besides the horizon locations, the identified transition characterization in the interpretation is associated with geological processes. In this way, a possible transition classification could be linked to the seismic facies. Detection of transitions or singularities in signals is based on simple mathematical concepts. The signal inflection points are associated with the first-derivative extremes which correspond to the second-derivative zero crossings. For the signal inflection-point positions, using the CWT local peak locations, a wavelet should be chosen as the first-derivative of the smoothing function. One of the wavelet functions that fulfill this requirement is the first-derivative of the Gaussian function, called the Gauss wavelet. We can extract scalogram's local peaks coincide from the signal inflection points. It can be proven that these lines, which are called WTMMLA, can be used to characterize the signal irregularity. The signal irregularities can be characterized mathematically through the WTMMLA and Hölder exponent (Mallat, 1999). The exponent can be obtained from the slope estimation of the curve created by the log2 of the WTMMLA coefficients divided by the log2 of the scales. In This study we used WTMMLA as a direct seismic attribute. We calculated CWT coefficients and WTMMLA for sixteen seismic data samples around the picked reservoir horizon. The extracted WTMMLA can show the possible heterogeneity and singularity within the reservoir. We used these attribute as input vector for the SOM step and obtained the U_matrix. The K-mean and DBI estimate the number of seismic facies.Utilizing of CWT to locate events in time through the identification of signal singularities also proved to be useful as an appropriate tool for detection of seismic events. Therefor this method proved to be less sensitive to interpretation errors. The performance of the method was tested on Kangan formation at one of the Iranian oil fields.

Most geologic changes have a seismic response but sometimes this is expressed only in certain spectral ranges hidden within the broadband data. Spectral decomposition is one of the methods which can be utilized to help interpreting such cases. There are several time-frequency methods including: short-time Fourier transform (STFT), continuous wavelet transform (CWT), Wigner-Ville distribution (WVD), S-transform, and matching pursuit decomposition (MPD). In this paper, we use the MPD method. This method is newer than the other time-frequency methods used in exploration seismology. Mallat and Zhang (1993) have improved time and frequency resolution simultaneously by using MPD method. In this method, a signal is decomposed into constructive wavelets. Time and frequency properties of wavelets are used locally for spectral decomposition. Pursuits are the algorithms which search the best time-frequency matching between the signal and a linear combination of selected wavelets from wavelet dictionary. Matching pursuit which is an iterative procedure optimizes signal estimation by each new wavelet chosen from a dictionary. These wavelets combined linearly to obtain the best match with the signal. A signal should expand to waveforms which their time-frequency properties could be matched to local structures. Such waveforms are called time-frequency atoms. There are many approaches to match wavelets of dictionary to a seismic signal and to obtain time-frequency spectrum in matching pursuit decomposition. The base of all approaches is the Mallat and Zhang’s algorithm. However, computing time of the original algorithm is very high due to many iterations and that is why particular conditions have been applied in different researches to limit the matching pursuit algorithm for obtaining the lower performing time. In this work, particular conditions are rather similar to Wang’s (2007) method. On seismic data, layer thickness is described on the basis of the seismic travel time. When a layer with different properties has a thickness by one-fourth wavelength, top and base reflections will interfere constructively. For thin layers less than tuning thickness, combined seismic amplitude decreases with thickness (when reflection coefficients of the top and of the base are opposite). Generally, spectral decomposition has many applications in interpretation of seismic sections and so there will be extra needs to study and develop them. Thin layers and many stratigraphic hydrocarbon reservoirs are beneath the threshold of the vertical seismic resolution (tuning thickness) and because of their low thicknesses, they are not resolvable. For this reason, mapping the small-scale geological structures is one of the important interpretational cases. When the thickness of a thin layer decreases pick frequency slightly increases. In this work, this issue has been used to detect thin layers by time-frequency spectrum and by single-frequency sections obtained from MPD. In this paper, we investigated the performance of the matching pursuit decomposition for time-frequency analysis of seismic sections to delineate and detect thin layers on synthetic data (including simple thin layer model and also wedge model) and real data. It is observed that the interpretation of thin layers is simpler by single-frequency sections. It is shown that for a simple thin layer if considerable frequency in single-frequency sections increases, ability in resolving layer interfaces would be increased. In the wedge model, as the frequency increases resolution threshold of layer interfaces moves to a lower thickness and therefore it would be possible to detect lower thickness layers. The tuning thickness has been decreased from 19 meters in original seismic section to 12 meters in 80 Hz frequency section. In the real data, it is shown that when a thin layer is not resolvable in a seismic section it might be detected using the MPD method. In this case, by providing 20, 40, 60, 80 and 100 Hz single-frequency sections when high frequency sections are studied, interfaces of thin layer are appeared gradually. It is concluded that time-frequency sections are useful instruments to detect and delineate thin layers.